Review of cutting-edge sensing technologies for urban underground construction

Ma, Enlin; Lai, Jinxing; Wang, Lixin; Wang, Ke; Xu, Shuoshuo; Li, Chujun; Guo, Chunxia

doi:10.1016/j.measurement.2020.108289

Cited by 30 publications

(13 citation statements)

References 88 publications

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“…Tracking technologies enable easy data collection and easy data retrieval on-site (Yang et al. , 2015; Ma et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Tracking technologies enable easy data collection and easy data retrieval on-site (Yang et al, 2015;Ma et al, 2021). Tracking the location of construction resources automatically should both improve project performance and enable derivation of performance indicators, such as productivity (Mostafa and Hegazy, 2021;Schuldt et al, 2021).…”

Section: Tracking Technologiesmentioning

confidence: 99%

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A review of methodologies for performance evaluation of automated construction processes

Krishnamoorthi

Raphael

2021

BEPAM

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PurposeThe aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”Design/methodology/approachA systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.FindingsThe primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.Research limitations/implicationsThis study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.Originality/valueGap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.

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“…Tracking technologies enable easy data collection and easy data retrieval on-site (Yang et al. , 2015; Ma et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

Section: Tracking Technologiesmentioning

confidence: 99%

A review of methodologies for performance evaluation of automated construction processes

Krishnamoorthi

Raphael

2021

BEPAM

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“…e future structural deformation trend can be predicted by analyzing the monitoring historical data [38]. Combined with the advantages of the TCN, a structural deformation prediction model is proposed based on the TCN in this study, and the flowchart of the proposed method is shown in Figure 5.…”

Section: Structural Deformation Prediction Modelmentioning

confidence: 99%

“…In order to ensure the safety of the structure, the application of monitoring equipment is wide and essential, and the structural deformation monitoring data are obtained by the automatic monitoring equipment in a certain sampling interval, and it is a typical time sequence which reflects the rules of structural health changing in the time field. The future structural deformation trend can be predicted by analyzing the monitoring historical data [ 38 ]. Combined with the advantages of the TCN, a structural deformation prediction model is proposed based on the TCN in this study, and the flowchart of the proposed method is shown in Figure 5 .…”

Section: Temporal Convolutional Sequence Modelmentioning

confidence: 99%

A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks

Luo

Gan

Wang

et al. 2021

Computational Intelligence and Neuroscience

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The structural engineering is subject to various subjective and objective factors, the deformation is usually inevitable, the deformation monitoring data usually are nonstationary and nonlinear, and the deformation prediction is a difficult problem in the field of structural monitoring. Aiming at the problems of the traditional structural deformation prediction methods, a structural deformation prediction model is proposed based on temporal convolutional networks (TCNs) in this study. The proposed model uses a one-dimensional dilated causal convolution to reduce the model parameters, expand the receptive field, and prevent future information leakage. By obtaining the long-term memory of time series, the internal time characteristics of structural deformation data can be effectively mined. The network hyperparameters of the TCN model are optimized by the orthogonal experiment, which determines the optimal combination of model parameters. The experimental results show that the predicted values of the proposed model are highly consistent with the actual monitored values. The average RMSE, MAPE, and MAE with the optimized model parameters reduce 44.15%, 82.03%, and 66.48%, respectively, and the average running time is reduced by 45.41% compared with the results without optimization parameters. The average RMSE, MAE, and MAPE reduce by 26.88%, 62.16%, and 40.83%, respectively, compared with WNN, DBN-SVR, GRU, and LSTM models.

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“…During the construction of piles, load tests are important for investigating their actual field behavior. The continuous measurement of deformation along the pile, offered by distributed optical fiber sensors, helps to understand the real pile-soil interaction behavior [10]. Furthermore, thanks to their capability to detect both compressive and tensile strain with high sensitivity, distributed optical fiber sensors can be useful in monitoring early movements of soil slopes by the direct embedding of suitable fiber cables in the ground [11,12].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of a Tunnel in a Landslide Prone Area by Brillouin-Based Distributed Optical Fiber Sensors

Minardo

Catalano

Coscetta

et al. 2021

Sensors

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This paper shows the results of the monitoring of the deformations of a tunnel, carried out using a distributed optical fiber strain sensor based on stimulated Brillouin scattering. The artificial tunnel of the national railway crosses the accumulation zone of an active landslide, the Varco d’Izzo earthflow, in the southern Italian Apennines. Severely damaged by the landslide movements, the tunnel was demolished and rebuilt in 1992 as a reinforced concrete box flanked by two deep sheet pile walls. In order to detect the onset of potentially dangerous strains of the tunnel structure and follow their time trend, the internal deformations of the tunnel are also monitored by a distributed fiber-optic strain sensor since 2016. The results of the monitoring activity show that the deformation profiles are characterized by strain peaks in correspondence of the structural joints. Furthermore, the elongation of the fiber strands crossing the joints is consistent with the data derived by other measurement systems. Experiments revealed an increase in the time rate of the fiber deformation in the first and last part of the monitoring period when the inclinometers of the area also recorded an acceleration in the landslide movements.

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Review of cutting-edge sensing technologies for urban underground construction

Cited by 30 publications

References 88 publications

A review of methodologies for performance evaluation of automated construction processes

A review of methodologies for performance evaluation of automated construction processes

A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks

Long-Term Monitoring of a Tunnel in a Landslide Prone Area by Brillouin-Based Distributed Optical Fiber Sensors

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